4,777 research outputs found
A Cascade Neural Network Architecture investigating Surface Plasmon Polaritons propagation for thin metals in OpenMP
Surface plasmon polaritons (SPPs) confined along metal-dielectric interface
have attracted a relevant interest in the area of ultracompact photonic
circuits, photovoltaic devices and other applications due to their strong field
confinement and enhancement. This paper investigates a novel cascade neural
network (NN) architecture to find the dependance of metal thickness on the SPP
propagation. Additionally, a novel training procedure for the proposed cascade
NN has been developed using an OpenMP-based framework, thus greatly reducing
training time. The performed experiments confirm the effectiveness of the
proposed NN architecture for the problem at hand
Sleep Spindle Detection by Using Merge Neural Gas
In this paper the Merge Neural Gas (MNG) model is applied to detect sleep spindles in EEG. Features are extracted from windows of the EEG by using short time Fourier transform. The total power spectrum is computed in six frequency bands and used as input to the MNG network. The results show that MNG outperforms simple neural gas in correctly detecting sleep spindles. In addition the temporal quantization results as well as sleep trajectories are visualized on two-dimensional maps by using the OVING projection method
Visual region understanding: unsupervised extraction and abstraction
The ability to gain a conceptual understanding of the world in uncontrolled environments is the ultimate goal of vision-based computer systems. Technological
societies today are heavily reliant on surveillance and security infrastructure, robotics, medical image analysis, visual data categorisation and search, and smart device user interaction, to name a few. Out of all the complex problems tackled
by computer vision today in context of these technologies, that which lies closest to the original goals of the field is the subarea of unsupervised scene analysis or scene modelling. However, its common use of low level features does not provide
a good balance between generality and discriminative ability, both a result and a symptom of the sensory and semantic gaps existing between low level computer
representations and high level human descriptions.
In this research we explore a general framework that addresses the fundamental
problem of universal unsupervised extraction of semantically meaningful visual
regions and their behaviours. For this purpose we address issues related to
(i) spatial and spatiotemporal segmentation for region extraction, (ii) region shape modelling, and (iii) the online categorisation of visual object classes and the spatiotemporal analysis of their behaviours. Under this framework we propose (a)
a unified region merging method and spatiotemporal region reduction, (b) shape
representation by the optimisation and novel simplication of contour-based growing neural gases, and (c) a foundation for the analysis of visual object motion properties using a shape and appearance based nearest-centroid classification algorithm
and trajectory plots for the obtained region classes.
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Specifically, we formulate a region merging spatial segmentation mechanism
that combines and adapts features shown previously to be individually useful,
namely parallel region growing, the best merge criterion, a time adaptive threshold, and region reduction techniques. For spatiotemporal region refinement we
consider both scalar intensity differences and vector optical flow. To model the shapes of the visual regions thus obtained, we adapt the growing neural gas for
rapid region contour representation and propose a contour simplication technique. A fast unsupervised nearest-centroid online learning technique next groups observed region instances into classes, for which we are then able to analyse spatial
presence and spatiotemporal trajectories. The analysis results show semantic correlations to real world object behaviour. Performance evaluation of all steps across
standard metrics and datasets validate their performance
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
The neural modelling of a direct reduction process
M.Ing. (Mechanical)The goal of this study was to determine whether a SLIRN direct reduction process could be modelled with a neural network. The full name of the SLIRN process is the Stelco, Lurgi, Republic Steel, and National Leadprocess. A parallel goal was to identify, and test an alternative method to reduce the dimensionality of a model. A neural network software package named Process Insights was used to model the process. Two independent data reduction methods were used along with various Process Insights functions, to build, train, and test models. The best model produced by each of the two data reduction methods was used to report on. The results showed that a SLIRN direct reduction process could be modelled successfully with a neural network. The large number of variables normally identified with such a process can be reduced without significant loss in model performance, The results also showed that the removal of the most significant variable does not affect the model accuracy significantly, which bodes well for the fault tolerance of the model in terms of individual sensor failures. The Process Insights functions important to the modelling process were highlighted
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